Optimal configuration method for hybrid energy storage of grid-connected wind storage power generation system

1. An optimal configuration method for a hybrid energy storage of a grid-connected wind storage power generation system, comprising: performing a frequency domain decomposition on a historical wind power output, to count a high-frequency component and a low-frequency component of the historical wind power output, and determining a rated power of the hybrid energy storage based on a probability distribution function; establishing a hybrid energy storage capacity optimization model for a full life cycle of a wind farm to minimize a net present value of an annual cost and maximize a target satisfaction rate (TSR) of an output; extracting a daily typical scenario for the wind power output based on a clustering algorithm, to count a time proportion of each typical scenario as an input scenario of the hybrid energy storage capacity optimization model for the full life cycle of the wind farm; solving with a multi-objective optimization algorithm, to obtain an optimal hybrid energy storage capacity configuration scheme for the grid-connected wind storage power generation system; and operating the grid-connected wind storage power generating system based on the optimal hybrid energy storage capacity configuration scheme,

wherein an objective function F 1 is established to minimize the net present value of the annual cost:

F

1

=

minimize

⁢

∑

k

=

1

2

(

C

Ik

+

∑

t

=

1

N

k

C

R

⁢

k

(

1

+

r

-

I

)

n

k

,

t

)

,

wherein

N

k

=

[

∑

j

=

1

J

S

j

,

k

·

p

j

·

L

·

365

d

k

·

C

k

·

c

yk

]

-

1

;

C

Rk

=

α

·

β

n

·

C

Ik

=

α

·

β

n

·

A

k

·

C

k

;

wherein, C Ik is an initial cost of a k-th energy storage device, in CNY; C Rk is a replacement cost of the k-th energy storage device, in CNY; N k is a replacement time of the k-th energy storage device, in hours; r is a discount rate for converting a future fund into a present value; I is an inflation rate; n k,t is a period for a k-th replacement of the energy storage device, in years; a is a ratio of the replacement cost to the initial cost; β n is a cost reduction coefficient of an energy storage material in an n-th year; s j,k is a stored electric quantity of the k-th energy storage device in a j-th typical scenario, in MWh; P j is a time proportion of the j-th typical scenario; L is an operational lifespan of the wind farm, in years; d k is a discharge depth of the k-th energy storage device; C k is a capacity of the k-th energy storage device, in MWh; c yk is a cycle number of the energy storage device; A k is a unit system cost of the k-th energy storage device, in CNY/MWh; J is a total number of the typical scenarios.

2. The optimal configuration method for the hybrid energy storage of the grid-connected wind storage power generation system according to claim 1 , wherein the frequency domain decomposition on the historical wind power output comprises a wavelet decomposition, a Kalman filtering and/or an empirical mode decomposition.

3. The optimal configuration method for the hybrid energy storage of the grid-connected wind storage power generation system according to claim 1 , wherein the clustering algorithm comprises a K-means clustering method, a hierarchical clustering method and/or a Gaussian mixture model method.

4. The optimal configuration method for the hybrid energy storage of the grid-connected wind storage power generation system according to claim 1 , wherein an objective function F 2 is established to maximize the TSR of the output:

F

2

=

maximize

⁢

∑

j

=

1

J

p

j

·

TSR

j

,

wherein

TS

⁢

R

j

=

∑

i

=

1

T

ω

i

,

j

T

;

ω

i

,

j

=

{

1

P

i

,

j

=

P

i

,

j

_

0

P

i

,

j

≠

P

i

,

j

_

;

wherein, TSR j is a TSR of a hybrid energy storage system in the j-th typical scenario; T is a total running period, in min; ω i,j is a determination coefficient of whether a system power output is equal to a target power at an i-th minute of the j-th typical scenario; P i,j is the system power output at the i-th minute of the j-th typical scenario; P i,j is the target power at the i-th minute of the j-th typical scenario; J is a total number of the typical scenarios; P j is a time proportion of the j-th typical scenario.

5. The optimal configuration method for the hybrid energy storage of the grid-connected wind storage power generation system according to claim 1 , wherein the hybrid energy storage capacity optimization model for the full life cycle of the wind farm has constraints, comprising:

an energy storage charge and discharge power constraint:

{

P

c

,

min

<

P

c

,

t

<

P

c

,

max

P

d

,

min

<

P

d

,

t

<

P

d

,

max

;

a power balance constraint:

S

⁢

O

⁢

C

t

=

{

S

⁢

O

⁢

C

t

-

1

+

η

c

⁢

P

c

,

t

⁢

Δ

⁢

t

/

S

Wh

S

⁢

O

⁢

C

t

-

1

-

P

d

,

t

⁢

Δ

⁢

t

/

(

S

Wh

⁢

η

d

)

;

and

an energy storage state of charge (SOC) constraint: SOC min ≤SOC t ≤SOC max ;

wherein, P c,t is an energy storage charge power at time t, in MW; P d,t is an energy storage discharge power at time t, in MW; P c,min is a lower limit of the energy storage charge power, in MW; P d,min is a lower limit of the energy storage discharge power, in MW; P c,max is an upper limit of the energy storage charge power, in MW; P d,max is an upper limit of the energy storage discharge power, in MW; η c is an energy storage charge efficiency; η d is an energy storage discharge efficiency; Δt is a control interval, in min; S Wh is a rated capacity of the energy storage, in MW; SOC t is an energy storage at time t; SOC t-1 is an energy storage at time t−1; SOC min is a lower limit of the energy storage; SOC max is an upper limit of the energy storage.

6. The optimal configuration method for the hybrid energy storage of the grid-connected wind storage power generation system according to claim 1 , wherein PRR Δt is set as a smoothing index for a target power curve of wind power; PRR Δt is used to evaluate a fluctuation smoothing effect of the target power curve of the wind power, and PRR Δt is calculated as follows:

P

⁢

R

⁢

R

Δ

⁢

t

=

P

max

,

Δ

⁢

t

-

P

min

,

Δ

⁢

t

P

R

×

100

⁢

%

;

wherein, P max,Δt is a maximum wind power output during Δt, in MW; P min,Δt is a minimum wind power output during Δt, in MW; P R is a rated power of the wind farm.

7. The optimal configuration method for the hybrid energy storage of the grid-connected wind storage power generation system according to claim 1 , wherein the multi-objective optimization algorithm comprises a dynamic programming algorithm or a heuristic algorithm.

8. The optimal configuration method for the hybrid energy storage of the grid-connected wind storage power generation system according to claim 7 , wherein the heuristic algorithm comprises a genetic algorithm, an artificial neural network and an ant colony algorithm.